I build local AI infrastructure, evidence-gated automation, and operator tooling for systems that need to be understandable, testable, and recoverable.

My work connects physical infrastructure, distributed compute, networking, model operations, validation, and technical documentation. I focus on the part that is often skipped: proving what a system did, defining when automation must refuse to act, and leaving behind artifacts another engineer can inspect.

• Local multi-system AI infrastructure and operating methods
• Evidence-gated network diagnostics and change planning
• Guarded recovery systems with refusal, cooldown, verification, and rollback
• Reproducible model-serving, quantization, and benchmark workflows
• Privacy-safe engineering documentation, case studies, and publication checks

Each project below is a clean public publication surface. The case studies, architecture documents, synthetic JSON artifacts, and automated safety checks are available in the repository - not hidden behind a summary claim.

1. Prove identity and current state before acting.
2. Separate observations, measurements, plans, actions, results, and unknowns.
3. Define acceptance criteria and rollback before risky work.
4. Require evidence gates for changes; refuse when evidence is incomplete.
5. Treat a green test as evidence for a specific claim, not proof of everything.
6. Publish the method and artifacts without publishing the live environment.

The diagram below is fictional and uses reserved documentation addresses. It demonstrates the engineering pattern only; it does not reproduce or approximate a live topology.

flowchart LR
    operator["Operator workspace<br/>control.example<br/>192.0.2.10"]
    evidence["Evidence and planning<br/>evidence.example<br/>198.51.100.20"]
    gate{"Policy and safety gates"}
    target["Synthetic compute target<br/>compute.example<br/>203.0.113.30"]
    receipt["Validation and receipts"]

    operator --> evidence
    evidence --> gate
    gate -->|approved plan| target
    gate -->|insufficient evidence| operator
    target --> receipt
    receipt --> evidence

The public repositories are built so a reader can inspect more than prose:

• Case studies explain the problem, engineering choices, evidence, and limitations.
• Architecture documents show the public-safe system model.
• JSON examples make contracts and decision records concrete.
• Standard-library safety checkers scan every repository for prohibited private data.
• GitHub Actions rerun the publication-safety check on every change.

The examples are intentionally synthetic. They support review of the method without creating live deployment claims.

I do not publish live addresses, hostnames, hardware identifiers, account names, local paths, credentials, raw telemetry, service inventories, physical layouts, outlet maps, controller identities, or operational topology. Public examples use reserved documentation addresses and fictional names. Future technical material is added only after privacy review.

For project-specific questions, open an issue in the relevant public repository.